Self-Supervised Linear Motion Deblurring

  title={Self-Supervised Linear Motion Deblurring},
  author={Peidong Liu and Joel Janai and Marc Pollefeys and Torsten Sattler and A. Geiger},
  journal={IEEE Robotics and Automation Letters},
  • Peidong Liu, Joel Janai, +2 authors A. Geiger
  • Published 2020
  • Computer Science
  • IEEE Robotics and Automation Letters
  • Motion blurry images challenge many computer vision algorithms, e.g., feature detection, motion estimation, or object recognition. Deep convolutional neural networks are state-of-the-art for image deblurring. However, obtaining training data with corresponding sharp and blurry image pairs can be difficult. In this letter, we present a differentiable reblur model for self-supervised motion deblurring, which enables the network to learn from real-world blurry image sequences without relying on… CONTINUE READING


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    • 27
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    • 148
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    • 536
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    • 72
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    • 88
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    Unsupervised Monocular Depth Estimation with Left-Right Consistency
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    • 313
    • PDF